Human-Exploratory-Procedure-Based Hybrid Measurement Fusion for Material Recognition

While various biomimetic robotic haptic sensors are often utilized to measure multifarious physical interactions to identify material properties under human exploratory procedures (EPs), traditional methods are unable to fuse well multimodal measurements under EPs. In order to solve this problem, an innovative hybrid joint group kernel sparse coding model for material recognition under EPs is proposed. First, a series of feature representations according to the characteristics of different measures are introduced. Second, we propose an innovative hybrid fusion framework based on statistical learning with kernel sparsity to effectively fuse the measurements from robotic EPs. Third, a matched optimization algorithm is constructed to deal with the innate optimization problem in the proposed framework. Finally, we develop an evaluation based on three different classification levels (coarse, medium, and fine) with the public dataset containing 184 material classes to verify the proposed method and compare with some other similar kernel methods. The evaluation results show that the accuracy of coarse, medium, and fine classification of the proposed hybrid fusion framework is 98.4%, 96.2%, and 98.4%, respectively. The framework can be extended easily to other fusion tasks with multiple measurements.